Skip to main content

Using Overall Equipment Effectiveness (OEE) to Predict Shutdown Maintenance

  • Conference paper
  • First Online:
Engineering Systems and Networks

Abstract

This research proposes an approach to predict equipment condition using OEE performance metrics. Statistical tools are used to correlate measurements of OEE factors and maintenance history from a real database. The results suggesting that there is a correlation between the Time Between Stoppages and the trend degree of the Mean and/or the Standard Deviation (SD) of cycle time. This approach intended to help the predictions of shutdowns for maintenance.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Abeygunawardane SK, Jirutitijaroen P, Xu H (2013) Adaptive maintenance policies for aging devices using a Markov decision process. IEEE 28(3):3194–3203

    Google Scholar 

  • Ahmad R, Kamaruddin S (2012) An overview of time-based and condition-based maintenance in industrial application. Comput Ind Eng 63:35–149

    Article  Google Scholar 

  • Ahmad R, Kamaruddin S (2013) Maintenance decision-making process for a multi-component production unit using output-based maintenance technique: a case study for non-repairable two serial components. Unit Int J Performability Eng 9(3):305–319

    Google Scholar 

  • Almeanazel OTR (2010) Total productive maintenance review and overall equipment effectiveness measurement. Jordan J Mech Ind Eng 4(4):517–522

    Google Scholar 

  • Jardine AKS, Lin D, Banjevic D (2006) A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech Syst Signal Process 20(7):1483–1510

    Article  Google Scholar 

  • Kumar U, Galar D, Parida A, Stenstrom C, Berges L (2013) Maintenance performance metrics: a state-of-the-art review. J Qual Maintenance Eng 19(3):233–277

    Article  Google Scholar 

  • Kurscheidt Netto RJ, Santos EAP, Loures ER, Pierezan R (2014) Condition-based maintenance using OEE: an approach to failure probability estimation. In: Proceedings of 7th international conference on production research—Americas 2014, Lima

    Google Scholar 

  • Maurya MR, Paritosh PK, Rengaswamy R, Venkatasubramanian V (2010) A framework for on-line trend extraction and fault diagnosis. Eng Appl Artif Intell 23(6):950–960

    Article  Google Scholar 

  • Nakajima S (1988) An introduction to TPM: total productive maintenance. Productivity Press, Portland

    Google Scholar 

  • Rozinat A, Mans RS, Song M, Van Der Aalst WMP (2009) Discovering simulation models. Inf Syst 34(3):305–327

    Google Scholar 

  • Santos EAP, De Freitas RL, Deschamps F, De Paula MAB (2008) Proposal of an industrial information system model for automatic performance evaluation. In: IEEE international conference on emerging technologies and factory automation, pp 436–439

    Google Scholar 

  • Van Der Aalst WMP, Schonenberg MH, Song M (2011) Time prediction based on process mining. Inf Syst 47(2):237–267

    Google Scholar 

  • Venkatasubramanian V, Rengaswamy R, Kavuri SN, Yin K (2003) A review of process fault detection and diagnosis: part III: process history based methods. Comput Chem Eng 27(3):327–346. Elsevier

    Google Scholar 

  • Wang W (2012) An overview of the recent advances in delay-time-based maintenance modeling. Reliab Eng Syst Safe 106:165–178

    Google Scholar 

  • Weber P, Bordbar B, Tino P, Majeed B (2011) A framework for comparing process mining algorithms. In: GCC conference and exhibition (GCC), 2011 IEEE, pp 625–628

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Rolando Jacyr Kurscheidt Netto .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing Switzerland

About this paper

Cite this paper

Kurscheidt Netto, R.J., Santos, E.A.P., de Freitas Rocha Loures, E., Pierezan, R. (2017). Using Overall Equipment Effectiveness (OEE) to Predict Shutdown Maintenance. In: Amorim, M., Ferreira, C., Vieira Junior, M., Prado, C. (eds) Engineering Systems and Networks. Lecture Notes in Management and Industrial Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-45748-2_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-45748-2_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-45746-8

  • Online ISBN: 978-3-319-45748-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics